What is the "Hello World" problem of Reinforcement Learning?
MNIST (along with CIFAR) may be the &quot;Hello World&quot; of supervised learning for image classification, but it is definitely not the &quot;Hello World&quot; of all machine learning techniques, given that RL is also part of ML and MNIST is definitely not the &quot;Hello World&quot; of RL.
I don't think there is a single &quot;Hello World&quot; problem for RL. However, if you are looking for simple problems (or environments) that are usually used as baselines to assess the quality of RL agents, then I would say that the simple grid worlds where you need to move from one place to the other, the CartPole, MountainCar, Pendulum or other environments listed here are often used.
The environment that you choose to train and test your RL agent depends on your goals. For example, if you designed an algorithm that is supposed to deal with continuous action spaces, then an environment where you can take only a discrete number of actions may not be a good option.
The mentioned environments are very simple (i.e. toy problems). In my opinion, we need more serious environments that can show the applicability of RL to other areas other than (relatively simple) games.